Imaging and image processing for computer-aided diagnosis and improved patient care are areas of interest to medical and dental practitioners. 3-D volume imaging has been a diagnostic tool that offers advantages over earlier 2-D (two dimensional) radiographic imaging techniques for identifying and evaluating the condition of internal structures and organs. 3-D imaging of a patient or other subject has been made possible by a number of advancements, including the development of high-speed imaging detectors, such as digital radiography (DR) detectors that enable multiple images to be taken in rapid succession. Digital volume images, obtained from computerized tomography (CT) or other imaging systems, provide tools for diagnosis, treatment planning, and biomedical modeling and visualization.
Among areas of particular interest for computer-aided diagnosis, treatment assessment, and surgery is image segmentation, particularly for tooth regions. Among approaches that have been proposed for tooth segmentation is that described by Shah et al. in “Automatic tooth segmentation using active contour without edges”, 2006, IEEE Biometrics Symposium. The authors describe a method for automating identification of teeth based on dental characteristics from multiple digitized dental records, using an estimate of tooth contour in order to permit efficient feature extraction. It has been found, however, that extracting the contour of the teeth from other image content is a challenging task. In Shah's method, tooth contour estimation is accomplished using the active contour without edges, based on the intensity of the overall region of the tooth image. For a number of reasons, results of such processing demonstrate limited success in tackling this problem.
In an article entitled “Teeth and jaw 3-D reconstruction in stomatology”, Proceedings of the International Conference on Medical Information Visualisation—BioMedical Visualisation, pp 23-28, 2007, researchers Krsek et al. describe 3-D geometry models of teeth and jaw bones based on input CT image data. The input discrete CT data are segmented by a substantially automated procedure, with manual verification and correction, as needed. Creation of segmented tissue 3-D geometry models is based on vectorization of input discrete data extended by smoothing and decimation. Segmentation is based primarily on selecting a threshold of Hounsfield Unit (HU) values and provides segmentation results in some cases. However, this method has not proved to be sufficiently robust for practical use.
Akhoondali et al. propose a fast automatic method for the segmentation and visualization of teeth in multi-slice CT-scan data of the patient's head in an article entitled “Rapid Automatic Segmentation and Visualization of Teeth in CT-Scan Data”, Journal of Applied Sciences, pp 2031-2044, 2009. In the algorithm described therein, the mandible and maxilla are first separated using maximum intensity projection in the y direction and a step-like region separation algorithm. Next, the dental region is separated using maximum intensity projection in the z direction, thresholding, and cropping. Teeth are then segmented using a region growing algorithm based on multiple thresholds that distinguish between seed points, teeth and non-tooth tissue. Results are visualized using iso-surface extraction and surface and volume rendering. A semi-automatic method is also proposed for rapid metal artifact removal. However, in practice, it is difficult to identify the needed threshold values for a proper segmentation operation.
In an article entitled “Automatic Tooth Region Separation for Dental CT Images”, Proceedings of the 2008 Third International Conference on Convergence and Hybrid Information Technology, pp 897-901, (2008), researchers Gao et al. disclose a method to construct and visualize the individual tooth model from CT image sequences. This method attempts to separate teeth for CT images wherein the teeth touch each other in some slices. The method finds the individual region for each tooth and separates two teeth if they touch. The method described initially separates upper and lower tooth regions and fits the dental arch. A plane is projected along each arch point and the corresponding integral intensity is computed. The resulting values are then used to draw a profile and determine the position of the separating plane. The position identification of the tooth region can guide the segmentation of individual tooth contours in 2-D space and tooth surface in 3-D space. However, methods of this type often fail to separate the teeth correctly; often the cut lines extend across the teeth rather than along tooth edges.
Various methods have been proposed for improving interactive segmentation. For example, Kang et al., in a recently published article entitled “Interactive 3-D editing tools for image segmentation”, Medical Image Analysis, 8 (2004), pp. 35-46, describe an interactive 3-D editing tool for image segmentation. Editing tools are used to identify a volume of interest (VOI) in a 3-D image and to correct errors in initial automatic segmentation procedures. A viewer uses the editing tools to position and size a spherical volume of interest; and the spherical geometric primitive is visualized using other geometric primitives (possibly with different dimensionality) in separate multi-planar reformations. The user interface described by Kang et al. shows orthogonal presentations of axial, sagittal, and coronal views in three different viewing windows. This method of providing input to segmentation routines can be useful for regions whose shape is spherical, but is of less value for providing information related to actual anatomical features. Its presentation mode requires the user to mentally reconstruct the original geometric primitive using the three standard orthogonal views.
As 3-D data is often presented in its projection form in a 2-D space, such as on a display screen, one concern relates to intuitive design and convenience for supporting effective 3-D data contouring. Conventionally, a slice-by-slice method is used to perform contouring work, outlining the contour or drawing seed curves on a plurality of the 2-D cross-sectional images. These contours traced in the 2-D images are then assembled into 3-D space. However, in some cases, significant features are not revealed in the axial direction of the parallel slices (or cross-sectional images) but are more visible when viewed from other directions.
In 3-D interactive contouring, rapid response and visual feedback of operation results is required in order to make corrections by adding or removing geometric primitives such as curves, lines or points. Among other advantages, efficient operation and high speed processing helps to make the interactive contouring workflow more natural and easier for the operator.
Thus, it is seen that there is a need for a method that provides an improved interactive contouring for medical images